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Current Status of Computer-Aided Drug Design for Type 2 Diabetes

View Article: PubMed Central

ABSTRACT

Background: Diabetes is a metabolic disorder that requires multiple therapeutic approaches. The pancreas loses its functionality to properly produce the insulin hormone in patients with diabetes mellitus. In 2012, more than one million people worldwide died as a result of diabetes, which was the eighth leading cause of death.

Objective: Most drugs currently available and approved by the U.S. Food and Drug Administration cannot reach an adequate level of glycemic control in diabetic patients, and have many side effects; thus, new classes of compounds are required. Efforts based on computer-aided drug design (CADD) can mine a large number of databases to produce new and potent hits and minimize the requirement of time and dollars for new discoveries.

Methods: Pharmaceutical sciences have made progress with advances in drug design concepts. Virtual screening of large databases is most compatible with different computational methods such as molecular docking, pharmacophore, quantitative structure-activity relationship, and molecular dynamic simulation. Contribution of these methods in selection of antidiabetic compounds has been discussed.

Results: The Computer-Aided Drug Design (CADD) approach has contributed to successful discovery of novel anti-diabetic agents. This mini-review focuses on CADD approach on currently approved drugs and new therapeutic agents-in-development that may achieve suitable glucose levels and decrease the risk of hypoglycemia, which is a major obstacle to glucose control and a special concern for therapies that increase insulin levels.

Conclusion: Drug design and development for type 2 diabetes have been actively studied. However, a large number of antidiabetic drugs are still in early stages of development. The conventional target- and structure-based approaches can be regarded as part of the efforts toward therapeutic mechanism-based drug design for treatment of type 2 diabetes. It is expected that further improvement in CADD approach will enhance the new discoveries.

No MeSH data available.


The structure and activity of the potent hit (NSC83182) identified by testing compounds for inhibition of 11b-HSD1 mediated cortisol production in LS14 cell lysates with ChemGauss3 score = 84.17, % inhibition= 57.2, IC50 = 5.85 μM.
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Figure 3: The structure and activity of the potent hit (NSC83182) identified by testing compounds for inhibition of 11b-HSD1 mediated cortisol production in LS14 cell lysates with ChemGauss3 score = 84.17, % inhibition= 57.2, IC50 = 5.85 μM.

Mentions: The 11β-HSD1 is an enzyme that regulates glucocorticoid metabolism at the tissue level by converting the inert hormone cortisone to its active form, cortisol, in target tissues. Active cortisol production can cause insulin resistance by inhibiting pancreatic beta-cell insulin secretion and peripheral glucose uptake, and encourages gluconeogenesis [51]. Lagos et al. discovered novel 11β-HSD1 inhibitors by applying a combination of ligand and structure-based filtering methods. The NCI library of 260,071 structures was retrieved from the OpenNCI database [52] for the virtual screening and then filtered against ADMET constraints, and multiple conformations for each compound in the database were generated. Crystal structures of human 11β-HSD (PDB id: 1Y5R) in complex with inhibitors were used as the source of structural information shown in Fig. 6 (B). Virtual screening of the generated NCI conformer database was performed using the FRED version 2.2.5 program [31]. The top-scoring binding mode of 1,000 compounds on each protein binding site was considered, and the scoring scheme was based on three scoring functions: ChemGauss3, OE-Chem and Piecewise Linear Potential [53]. The 100 top-scoring compounds on each docking run were visually inspected, and overlap analysis of obtained solutions using the Tanimoto similarity metric was performed with InstantJChem v5.9 to select 40 compounds that were requested along with the ball grid array obtained from the Developmental Therapeutic Program [54] at NCI-NIH. The selected compounds were identified using in silico methods and further tested in cell-based assays to check cytotoxicity and 11β-HSD1-mediated cortisol production inhibitory efficiency. Biological testing was used in the identification of four compounds in adipocytes and steroid quantification by HPLC-MS/MS mediated cortisol production inhibitory activity, with potencies in the μM range. Two compounds proved to be potent and selective for 11β-HSD1 reductase activity and over the 11β-HSD2 isoform. This study leads to development of more active derivatives with higher efficacies to target intracellular cortisol levels in type 2 diabetes and other metabolic syndromes [55]. 11β-HSD1 inhibitor discovered by ligand- and structure-based virtual screening techniques with the best score and activity is shown in Fig. 3.


Current Status of Computer-Aided Drug Design for Type 2 Diabetes
The structure and activity of the potent hit (NSC83182) identified by testing compounds for inhibition of 11b-HSD1 mediated cortisol production in LS14 cell lysates with ChemGauss3 score = 84.17, % inhibition= 57.2, IC50 = 5.85 μM.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4997964&req=5

Figure 3: The structure and activity of the potent hit (NSC83182) identified by testing compounds for inhibition of 11b-HSD1 mediated cortisol production in LS14 cell lysates with ChemGauss3 score = 84.17, % inhibition= 57.2, IC50 = 5.85 μM.
Mentions: The 11β-HSD1 is an enzyme that regulates glucocorticoid metabolism at the tissue level by converting the inert hormone cortisone to its active form, cortisol, in target tissues. Active cortisol production can cause insulin resistance by inhibiting pancreatic beta-cell insulin secretion and peripheral glucose uptake, and encourages gluconeogenesis [51]. Lagos et al. discovered novel 11β-HSD1 inhibitors by applying a combination of ligand and structure-based filtering methods. The NCI library of 260,071 structures was retrieved from the OpenNCI database [52] for the virtual screening and then filtered against ADMET constraints, and multiple conformations for each compound in the database were generated. Crystal structures of human 11β-HSD (PDB id: 1Y5R) in complex with inhibitors were used as the source of structural information shown in Fig. 6 (B). Virtual screening of the generated NCI conformer database was performed using the FRED version 2.2.5 program [31]. The top-scoring binding mode of 1,000 compounds on each protein binding site was considered, and the scoring scheme was based on three scoring functions: ChemGauss3, OE-Chem and Piecewise Linear Potential [53]. The 100 top-scoring compounds on each docking run were visually inspected, and overlap analysis of obtained solutions using the Tanimoto similarity metric was performed with InstantJChem v5.9 to select 40 compounds that were requested along with the ball grid array obtained from the Developmental Therapeutic Program [54] at NCI-NIH. The selected compounds were identified using in silico methods and further tested in cell-based assays to check cytotoxicity and 11β-HSD1-mediated cortisol production inhibitory efficiency. Biological testing was used in the identification of four compounds in adipocytes and steroid quantification by HPLC-MS/MS mediated cortisol production inhibitory activity, with potencies in the μM range. Two compounds proved to be potent and selective for 11β-HSD1 reductase activity and over the 11β-HSD2 isoform. This study leads to development of more active derivatives with higher efficacies to target intracellular cortisol levels in type 2 diabetes and other metabolic syndromes [55]. 11β-HSD1 inhibitor discovered by ligand- and structure-based virtual screening techniques with the best score and activity is shown in Fig. 3.

View Article: PubMed Central

ABSTRACT

Background: Diabetes is a metabolic disorder that requires multiple therapeutic approaches. The pancreas loses its functionality to properly produce the insulin hormone in patients with diabetes mellitus. In 2012, more than one million people worldwide died as a result of diabetes, which was the eighth leading cause of death.

Objective: Most drugs currently available and approved by the U.S. Food and Drug Administration cannot reach an adequate level of glycemic control in diabetic patients, and have many side effects; thus, new classes of compounds are required. Efforts based on computer-aided drug design (CADD) can mine a large number of databases to produce new and potent hits and minimize the requirement of time and dollars for new discoveries.

Methods: Pharmaceutical sciences have made progress with advances in drug design concepts. Virtual screening of large databases is most compatible with different computational methods such as molecular docking, pharmacophore, quantitative structure-activity relationship, and molecular dynamic simulation. Contribution of these methods in selection of antidiabetic compounds has been discussed.

Results: The Computer-Aided Drug Design (CADD) approach has contributed to successful discovery of novel anti-diabetic agents. This mini-review focuses on CADD approach on currently approved drugs and new therapeutic agents-in-development that may achieve suitable glucose levels and decrease the risk of hypoglycemia, which is a major obstacle to glucose control and a special concern for therapies that increase insulin levels.

Conclusion: Drug design and development for type 2 diabetes have been actively studied. However, a large number of antidiabetic drugs are still in early stages of development. The conventional target- and structure-based approaches can be regarded as part of the efforts toward therapeutic mechanism-based drug design for treatment of type 2 diabetes. It is expected that further improvement in CADD approach will enhance the new discoveries.

No MeSH data available.